Chinese BertForTokenClassification Base Cased model (from ckiplab)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-chinese-pos is a Chinese model originally trained by ckiplab.

Predicted Entities

FW, Neqb, EXCLAMATIONCATEGORY, DE, Dk, COLONCATEGORY, VI, QUESTIONCATEGORY, DM, VF, VH, T, V_2, VE, Da, Cba, D, VD, Nd, A, SEMICOLONCATEGORY, Nv, VA, Neu, Nep, Nf, VC, Neqa, Di, PARENTHESISCATEGORY, Cbb, VL, VK, Nes, Nh, I, VG, VCL, DOTCATEGORY, SHI, PERIODCATEGORY, Na, Cab, PAUSECATEGORY, Caa, VAC, Ng, ETCCATEGORY, COMMACATEGORY, Ncd, Dfa, Nb, SPCHANGECATEGORY, P, Dfb, VHC, DASHCATEGORY, Nc, VB, VJ

Live Demo Open in Colab Download Copy S3 URI

How to use

documentAssembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")

tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_base_chinese_pos","zh") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("ner")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, tokenClassifier])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

result = pipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

val tokenizer = new Tokenizer()
    .setInputCols("document")
    .setOutputCol("token")

val tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_base_chinese_pos","zh")
    .setInputCols(Array("document", "token"))
    .setOutputCol("ner")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, tokenClassifier))

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)

Model Information

References

• https://huggingface.co/ckiplab/bert-base-chinese-pos
• https://github.com/ckiplab/ckip-transformers
• https://muyang.pro
• https://ckip.iis.sinica.edu.tw
• https://github.com/ckiplab/ckip-transformers
• https://github.com/ckiplab/ckip-transformers